Recent ucsd_cse_oapdeposits items

TDXRay: Microarchitectural Side-Channel Analysis of Intel TDX for Real-World Workloads

Fri, 17 Jul 2026 00:00:00 +0000

Confidential computing with VM-based trusted execution environments (TEEs) promises to protect code and data from a privileged cloud operator, enabling privacy-preserving workloads ranging from medical analytics to AI inference. However, most deployments exclude microarchitectural side channels from their threat model, shifting the burden to application developers who lack practical, general-purpose tools to assess (let alone mitigate) leakage. In particular, it remains unclear which host-observable signals persist under TDX's strict isolation and whether these signals can reveal sensitive information about confidential workloads. In this paper, we systematically investigate the side-channel attack surface in Intel TDX. We identify four new side-channel primitives: SEPTrace, Load+Probe, TSX-Probe, and MWAITProbe. Together, they expose page-level and cache-level activity with varying temporal precision. By combining these primitives, we construct TDXRay, a host-side measurement...

Poster: When Blocks Go Missing: The Timeliness and Trustworthiness of Blockchain RPC Providers

Fri, 17 Jul 2026 00:00:00 +0000

Contrary to blockchain's trustless vision, most applications built atop blockchain require trust in third-party Remote Procedure Call (RPC) providers. Applications rely on these RPC providers to be performant (announce new blocks timely) and reliable (no missing blocks/transactions) to provide good user experience and security guarantees. In this paper, we perform the first large-scale, longitudinal study to evaluate the timeliness and trustworthiness of 16 RPC providers for BNB Smart Chain (BSC) across 6 223 blocks and 123 773 transactions. We identify significant variability: some providers are inconsistent, miss valid blocks/transactions, or are seconds slower than others. Our findings suggest that the implicit trust assumptions are often violated, which may leave users confused and even vulnerable to attacks.

A Quantitative Analysis of Undergraduate Researchers' Intent to Apply to Graduate School

Fri, 17 Jul 2026 00:00:00 +0000

Understanding the factors that influence undergraduate students' intent to pursue graduate studies is crucial for developing effective academic pathways and increasing domestic participation in STEM graduate programs. Formal undergraduate research experiences, referred to as REUs, have the potential to enhance student confidence, promote a sense of belonging, and encourage persistence in STEM; however, their impact on intent to apply to graduate studies has not been thoroughly examined. This mixed-methods study investigates whether participation in REUs influences students' intentions to apply to graduate school by comparing national, Computing Research Association (CRA), and local, Carnegie Mellon University's Robotics Institute Summer Scholars (RISS), data. Analyses of CRA national surveys show that students with formal research experience are more than twice as likely to report an intent to apply to graduate school. Formal REU programs are highly effective educational interventions...

Likelihood-based optimization enables accurate copy number estimation for paralogous genes using exome data

Thu, 16 Jul 2026 00:00:00 +0000

MOTIVATION: Exome sequencing is widely used for genetic studies; however, accurate detection of copy number variants (CNV) in paralogous genes is challenging due to short-read mapping ambiguity and extensive copy-number variation. The human genome contains several hundred paralogous genes, many of which are known to harbor disease-associated CNVs. Existing exome CNV callers are primarily designed for rare CNV detection in uniquely mappable regions and are not well-suited for paralogous genes. METHODS: We describe a computational method (EdgeCopy) for copy number profiling of paralogous genes using whole-exome sequence data. EdgeCopy aggregates reads mapped to all copies of paralogous genes and relates observed read depth to copy number for multiple exome samples using an approximate composite likelihood function. The likelihood function is optimized using numerical optimization to obtain gene-level fractional copy number estimates that are discretized and refined using a Hidden...

Analysis of Gaze, Head Orientation, and Joint Attention in Autism With Triadic VR Interviews

Thu, 16 Jul 2026 00:00:00 +0000

Effective use of gaze and head orientation can strengthen the sense of inclusion in multi-party interactions, including job interviews. Not making significant eye contact with the interlocutors, or not turning towards them, may be interpreted as disinterest, which could worsen job interview outcomes. This study aims to support the situational solo practice of gaze behavior and head orientation using a triadic (three-way) virtual reality (VR) job interview simulation. The system lets users encounter common interview questions and see how they share attention among the interviewers based on their conversational role (speaking or listening). Given the yaw and position readings of the VR headset, we use a machine learning-based approach to analyze head orientations relative to the interviewers in the virtual environment, and achieve low angular error in a low complexity way. We examine the degree to which interviewer backchannels trigger attention shifts or behavioral mirroring and...

Designing for College Mental Health: We Have Resources; We Lack the Reach

Thu, 16 Jul 2026 00:00:00 +0000

The standard response to the student mental health crisis has been to expand clinical capacity by hiring more therapists and increasing funding. We challenge this approach. Through a two-part study with 154 undergraduate students and 9 campus providers at a large US university, we found that students considered physical and mental health a lower priority than other factors in their daily life. Universities offer a wide range of wellness resources, but most students simply unaware of their existence. Our findings suggest that the core issue is not necessarily a supply shortage, but rather fragmented access and limited visibility of resources. We argue that institutions should focus on building aggregator platforms that help students discover and navigate the support already available to them, as a means for a sustainable solution. This reframing suggests universities should rethink resource allocation: designing for student discovery and access to holistic well-being support beyond...

Gaze and Head Rotation Analysis in a Triadic VR Job Interview Simulation

Thu, 16 Jul 2026 00:00:00 +0000

Virtual reality (VR) systems have shown potential in analyzing human behavior across various domains. We present the design and development of a VR-based job interview simulation tailored for analyzing gaze and head rotation behaviors in a context with two virtual interviewers. Our system allows users to encounter common interview questions and quantifies how they share their attention (gaze and head rotations) to engage with multiple interviewers based on their conversational role (speaking or listening). We detect voice activity to identify the start of user speech and guide the backchannels (head nods or verbal cues such as "uh-huh") given by the virtual interviewers. We track the user’s gaze and use geometric yaw rotation adjustment given the yaw and position readings of the VR headset to find the head orientation of the user relative to the interviewers in the VR environment. The system enables the exploration of whether backchannels trigger an attention shift, or joint attention,...

Artificial Intelligence Note Summarization in the Emergency Department

Thu, 16 Jul 2026 00:00:00 +0000

This quality improvement study investigates the association of an electronic health record-integrated artificial intelligence note summarization tool with emergency physician medical record review time and user experience.

PIM-FW: Hardware-Software Co-Design of All-pairs Shortest Paths in DRAM

Thu, 16 Jul 2026 00:00:00 +0000

All-pairs shortest paths is a fundamental algorithm used for routing, logistics, and network analysis, but the cubic time complexity and heavy data movement of the canonical Floyd-Warshall algorithm severely limits its scalability on conventional CPUs or GPUs. In this paper, we propose PIM-FW, a novel co-designed hardware architecture and dataflow leveraging processing in and near memory to accelerate the blocked FW algorithm on an HBM3 stack. To enable fine-grained parallelism, we propose a massively parallel array of specialized bit-serial bank and channel PEs designed to accelerate core min-plus operations. Our dataflow complements this hardware, employing an interleaved mapping policy for superior load balancing and a hybrid memory computing model for efficient computation and reduction. This in-bank computing approach allows all distance updates to be performed and stored locally, a key contribution which eliminates the data-movement bottleneck inherent in GPU-based approaches....

Restriction Trees for Sparsity and Applications

Thu, 18 Jun 2026 00:00:00 +0000

Restriction Trees for Sparsity and Applications

Locally Computable High Independence Hashing

Thu, 18 Jun 2026 00:00:00 +0000

Locally Computable High Independence Hashing

Deep learning classification of reproductive tissue from ultrasound: sex determination in red abalone (Haliotis rufescens)

Thu, 18 Jun 2026 00:00:00 +0000

Accurate sex determination is critical for spawning success in both conservation breeding programs and commercial aquaculture, yet non-invasive methods remain limited in abalone species. Traditional approaches rely on visual inspection, which requires substrate detachment, can cause injury, and may induce premature gamete release. Here, we present the first application of machine learning to automate sex classification in red abalone (Haliotis rufescens) using non-invasive ultrasound imaging technology. We developed a labeled dataset of 246 high-quality ultrasound images from 44 individuals and benchmarked seven convolutional neural network architectures: VGG16, VGG19, ResNet50, ResNet101, YOLOv8, YOLOv11, and a custom convolutional neural network. Data partitioning by individual identity was essential to prevent artificially inflated accuracy from image leakage across splits. The YOLOv8 architecture achieved the highest test accuracy of 85.7% (precision: 0.905 male, 0.816 female;...

OMKar automates genome karyotyping using optical maps to identify constitutional abnormalities

Thu, 4 Jun 2026 00:00:00 +0000

The whole-genome karyotype refers to the sequence of large chromosomal segments comprising an individual's genotype. Karyotype analysis, which includes identifying aneuploidies and structural rearrangements, is essential for understanding genetic risk factors, informing diagnosis and treatment, and guiding genetic counseling in constitutional disorders. The current karyotyping standard relies on microscopic chromosome examination, a complex and expertise-dependent process with megabase-scale resolution. Optical genome mapping (OGM) technology offers an efficient approach to detect large-scale genomic lesions. Here, we introduce OMKar, a computational method that generates virtual karyotypes from OGM data. OMKar integrates structural variants (SVs) and copy number (CN) variants into a breakpoint graph representation. It re-estimates CNs using integer linear programming to enforce CN balance and then identifies constrained Eulerian paths corresponding to full chromosome structures....

Quantum Advantage from Sampling Shallow Circuits: Beyond Hardness of Marginals

Thu, 4 Jun 2026 00:00:00 +0000

Quantum Advantage from Sampling Shallow Circuits: Beyond Hardness of Marginals

microRNA-25 drives immune checkpoint therapy resistance by repressing innate and humoral immunity via Syndecan-3

Thu, 4 Jun 2026 00:00:00 +0000

Immune checkpoint therapy (ICT) can induce durable tumor control but is limited by primary and acquired resistance. The mechanisms underlying immune-resistant tumor microenvironments (TMEs) remain incompletely understood. Here we show that deletion of microRNA-25 (miR-25) sensitizes tumors to ICT across multiple syngeneic mouse models. Single-cell transcriptomics reveals that miR-25 deficiency activates innate and humoral immunity by increasing major histocompatibility complex class II (MHC II) expression in tumor-associated macrophages (TAMs) and enhancing classical complement signaling in cancer-associated fibroblasts (CAFs). Complement activation shifts CAFs toward an inflammatory (iCAF) state, reduces suppressive crosstalk with TAMs, and promotes a pro-inflammatory TME. Mechanistically, miR-25 represses Syndecan-3 (SDC3) in response to interferon-γ (IFN-γ). Editing the miR-25 binding site in Sdc3 restores SDC3 expression and overcomes resistance. These findings identify miR-25–mediated...

PTF Testing Lower Bounds for Non-Gaussian Component Analysis

Thu, 21 May 2026 00:00:00 +0000

This work studies information-computation gaps for statistical problems. A common approach for providing evidence of such gaps is to show sample complexity lower bounds (that are stronger than the information-theoretic optimum) against natural models of computation. A popular such model in the literature is the family of low-degree polynomial tests. While these tests are defined in such a way that make them easy to analyze, the class of algorithms that they rule out is somewhat restricted. An important goal in this context has been to obtain lower bounds against the stronger and more natural class of low-degree Polynomial Threshold Function (PTF) tests, i.e., any test that can be expressed as comparing some low-degree polynomial of the data to a threshold. Proving lower bounds against PTF tests has turned out to be challenging. Indeed, we are not aware of any non-trivial PTF testing lower bounds in the literature. In this paper, we establish the first non-trivial PTF testing lower...

The Orthogonal Vectors Conjecture for Branching Programs and Formulas

Thu, 21 May 2026 00:00:00 +0000

The Orthogonal Vectors Conjecture for Branching Programs and Formulas

Active Learning of General Halfspaces: Label Queries vs Membership Queries

Thu, 21 May 2026 00:00:00 +0000

Active Learning of General Halfspaces: Label Queries vs Membership Queries

On Fine-Grained Distinct Element Estimation

Thu, 21 May 2026 00:00:00 +0000

On Fine-Grained Distinct Element Estimation

Batch List-Decodable Linear Regression via Higher Moments

Thu, 21 May 2026 00:00:00 +0000

Batch List-Decodable Linear Regression via Higher Moments

Robustly Learning Mixtures of k Arbitrary Gaussians

Thu, 21 May 2026 00:00:00 +0000

We give a polynomial-time algorithm for the problem of robustly estimating a mixture of k arbitrary Gaussians in ${\mathbb {R}}^d $ , for any fixed k , in the presence of a constant fraction of arbitrary corruptions. This resolves the main open problem in several previous works on algorithmic robust statistics, which addressed the special cases of robustly estimating (a) a single Gaussian, (b) a mixture of TV-distance separated Gaussians, and (c) a uniform mixture of two Gaussians. Our main tools are an efficient partial clustering algorithm that relies on the sum-of-squares method, and a novel tensor decomposition algorithm that allows errors in both Frobenius norm and low-rank terms.

Clustering Mixtures of Bounded Covariance Distributions under Optimal Separation

Thu, 21 May 2026 00:00:00 +0000

We study the clustering problem for mixtures of bounded covariance distributions, under a fine-grained separation assumption. Specifically, given samples from a k-component mixture distribution D = Σki=1 wiPi, where each wi ≥ α for some known parameter α, and each Pi has unknown covariance Σi ≤ σ2i · Id for some unknown σi, the goal is to cluster the samples assuming a pairwise mean separation in the order of (σi+σj)/ √ α between every pair of components Pi and Pj. Our main contributions are as follows: • For the special case of nearly uniform mixtures, we give the first polynomial-time algorithm for this clustering task. Prior work either required separation scaling with the maximum cluster standard deviation (i.e. maxi σi) [DKK+22b] or required both additional structural assumptions and mean separation scaling as a large degree polynomial in 1/α [BKK22]. • For arbitrary (i.e. general-weight) mixtures, we point out that accurate clustering is information-theoretically impossible...

Efficient Multivariate Robust Mean Estimation Under Mean-Shift Contamination

Thu, 21 May 2026 00:00:00 +0000

Efficient Multivariate Robust Mean Estimation Under Mean-Shift Contamination

Implicit High-Order Moment Tensor Estimation and Learning Latent Variable Models

Thu, 21 May 2026 00:00:00 +0000

We study the general task of learning latent-variable models on ℝd with k hidden parameters. A common technique to address this task algorithmically is (some version of) the method of moments. Unfortunately, moment-based approaches are often hampered by the fact that the moment tensors of super-constant degree cannot even be written down in polynomial time. Motivated by such learning applications, we develop a general efficient algorithm for implicit moment tensor computation. Roughly speaking, our algorithm computes in poly(d, k) time a succinct approximate description of tensors of the form ${M_m} = \sum olimits_{i = 1}^k {{w_i}} v_i^{ \otimes m}$, for wi ∈ ℝ+—even for m = ω(1)—assuming that there exists an unbiased estimator for Mm with small variance that takes an appropriately nice form. Our framework broadly generalizes, both conceptually and technically, the work of [1] which developed an efficient algorithm for the specific moment tensors that arise in the task of clustering...

Robust Learning of Multi-index Models via Iterative Subspace Approximation

Thu, 21 May 2026 00:00:00 +0000

We study the task of learning Multi-Index Models (MIMs) in the presence of label noise under the Gaussian distribution. A K-MIM on ℝd is any function f that only depends on a K-dimensional subspace, i.e., f(x) = g(Wx) for a link function g on ℝK and a K × d matrix W. We consider a class of well-behaved MIMs with finite ranges that satisfy certain regularity properties. Our main contribution is a general noise-tolerant learning algorithm for this class whose complexity is qualitatively optimal in the Statistical Query (SQ) model. At a high-level, our algorithm attempts to iteratively construct better approximations to the defining subspace by computing low-degree moments of our function conditional on its projection to the subspace computed thus far, and adding directions with relatively large empirical moments. For well-behaved MIMs, we show that this procedure efficiently finds a subspace V so that f(x) is close to a function of the projection of x onto V, which can then be found...

Entangled Mean Estimation in High Dimensions

Thu, 21 May 2026 00:00:00 +0000

We study the task of high-dimensional entangled mean estimation in the subset-of-signals model. Specifically, given N independent random points x1,…,xN in D and a parameter α ∈ (0, 1) such that each xi is drawn from a Gaussian with mean µ and unknown covariance, and an unknown α-fraction of the points have identity-bounded covariances, the goal is to estimate the common mean µ. The one-dimensional version of this task has received significant attention in theoretical computer science and statistics over the past decades. Recent work has given near-optimal upper and lower bounds for the one-dimensional setting. On the other hand, our understanding of even the information-theoretic aspects of the multivariate setting has remained limited. In this work, we design a computationally efficient algorithm achieving an information-theoretically near-optimal error. Specifically, we show that the optimal error (up to polylogarithmic factors) is f(α,N) + √D/(α N), where the term f(α,N) is...

On Learning Parallel Pancakes with Mostly Uniform Weights

Thu, 21 May 2026 00:00:00 +0000

We study the complexity of learning k-mixtures of Gaussians (k-GMMs) on R^dThis task is known to have complexity d^Ω(k)in full generality. To circumvent this exponential lower bound on the number of components, research has focused on learning families of GMMs satisfying addi-tional structural properties. A natural assumption posits that the component weights are not expo-nentially small and that the components have the same unknown covariance. Recent work gave a d^{O(log(1/wmin))}-time algorithm for this class of GMMs, where Wmin is the minimum weight. Our first main result is a Statistical Query (SQ) lower bound showing that this quasi-polynomial upper bound is essentially best possible, even for the special case of uniform weights. Specifically, we show that it is SQ-hard to distinguish between such a mixture and the standard Gaussian. We further explore how the distribution of weights affects the complexity of this task. Our second main result is...

Faster Algorithms for Agnostically Learning Disjunctions and their Implications

Thu, 21 May 2026 00:00:00 +0000

Faster Algorithms for Agnostically Learning Disjunctions and their Implications

Understanding Mode Connectivity via Parameter Space Symmetry

Thu, 7 May 2026 00:00:00 +0000

Understanding Mode Connectivity via Parameter Space Symmetry

ROSCOE: Robot Scanning and Computing Equipment for Autonomous Terrestrial Mapping

Thu, 23 Apr 2026 00:00:00 +0000

Autonomous task-oriented robots are increasingly in demand across various domains; however, few existing systems address the challenge of autonomous high-resolution terrestrial scanning for construction and inspection purposes. This paper presents a task-oriented autonomy framework integrated with the Spot quadruped robot, enabling autonomous exploration, mapping, and deployment of a FARO terrestrial laser scanner. We introduce two novel algorithms for selecting optimal scanning positions: SCANSAFE (Scanpoint Navigator using Spatially-Aware Filtering and Evaluation), which prioritizes coverage of open space relative to prior scans, and PATHSAFE – Path-Aligned Trajectory Heuristic for Scanpoint Allocation with Filtering and Evaluation method, which places scan points along the robot’s traveled path. These approaches are evaluated against two existing strategies: Next-Best-View Greedy (NBV-Greedy) and Frontier, as well as a manually guided baseline. Tested in multiple environments,...

When Benchmarks Age: Temporal Misalignment through Large Language Model Factuality Evaluation

Fri, 10 Apr 2026 00:00:00 +0000

When Benchmarks Age: Temporal Misalignment through Large Language Model Factuality Evaluation

A Spectral Algorithm for List-Decodable Covariance Estimation in Relative Frobenius Norm

Thu, 9 Apr 2026 00:00:00 +0000

A Spectral Algorithm for List-Decodable Covariance Estimation in Relative Frobenius Norm

strong-bounds-for-skew-corner-free-sets

Thu, 26 Mar 2026 00:00:00 +0000

strong-bounds-for-skew-corner-free-sets

Quasipolynomial Bounds for the Corners Theorem

Thu, 12 Mar 2026 00:00:00 +0000

Let G be a finite abelian group and A be a subset of $G \times G$ which is corner-free, meaning that there are no $x, y \in G$ and $d \in G \backslash\{0\}$ such that $(x, y),(x+d, y),(x, y+d) \in A$. We prove that \begin{equation*}|A| \leq|G|^{2} \cdot \exp \left(-(\log |G|)^{\Omega{1}}\right)\end{equation*} As a consequence, we obtain polynomial (in the input length) lower bounds on the non-deterministic communication complexity of Exactly-N in the 3-player Number-on-Forehead model. We also obtain the first “reasonable” lower bounds on the coloring version of the 3 -dimensional corners problem, as well as on the non-deterministic communication complexity of Exactly-N in the 4-player Number-on-Forehead model. This is an extended abstract. The full version of the paper can be found at https://arxiv.org/abs/2504.07006.

Social Robots in Healthcare: Characterizing Privacy Considerations

Thu, 12 Mar 2026 00:00:00 +0000

As healthcare robots gain traction, human-robot interaction (HRI) researchers are exploring the factors that impact user adoption and trust in these robots. Due to the sensitive nature of care, privacy concerns play a significant role in determining robot utility, usefulness, and adoption. In our work, we conducted a 3x3x3 online study (N=239) to explore peoples' perceptions of privacy and utility of 3 robots at varying levels of Human-Likeness (HL) across 3 realistic healthcare contexts. The results show that the context of care delivery is a key driver of perceptions of privacy and acceptable privacy-utility trade-offs. Interestingly, the HL of robot design may not significantly impact peoples' privacy perceptions of healthcare robots. We plan to leverage these key findings to develop privacy-aware robot behaviors that are context adaptable in order to improve privacy outcomes for healthcare robots.

CAR-EM: A Synthesis-Based Clinically Assistive Robot System for Emergency Medicine

Thu, 12 Mar 2026 00:00:00 +0000

Emergency departments (EDs) are fast-paced, dynamic, safety-critical spaces where clinicians are overworked and underpaid. To support clinicians, researchers are exploring the contextualization and development of clinically assistive robots (CARs) that can assume non-critical tasks to reduce clinician overload. In this paper, we introduce CAR-EM (Clinically Assistive Robot System for Emergency Medicine), collaboratively developed with ED clinicians. CAR-EM includes an autonomous robot and a task specification interface. It completes tasks by leveraging control synthesis, a framework that automatically transforms high-level tasks into control while providing guarantees and feedback. We conducted a feasibility study across two different hospital EDs, where interprofessional clinicians tasked the robot to perform patient assessments and item deliveries. Clinicians found the system easy to use, and particularly helpful to offload busywork. This work demonstrates control synthesis...

The Command Line GUIde: Graphical Interfaces from Man Pages via AI

Fri, 30 Jan 2026 00:00:00 +0000

Although birthed in the era of teletypes, the command line shell survived the graphical interface revolution of the 1980’s and lives on in modern desktop operating systems. The command line provides access to powerful functionality not otherwise exposed on the computer, but requires users to recall textual syntax and carefully scour documentation. In contrast, graphical interfaces let users organically discover and invoke possible actions through widgets and menus. To better expose the power of the command line, we demonstrate a mechanism for automatically creating graphical interfaces for command line tools by translating their documentation (in the form of man pages) into interface specifications via AI. Using these specifications, our user-facing system, called GUIde, presents the command options to the user graphically. We evaluate the generated interfaces on a corpus of commands to show to what degree GUIDE offers thorough graphical interfaces for users’ real-world command...

A Realistic Radar Simulator for End-to-End Autonomous Driving in CARLA

Thu, 29 Jan 2026 00:00:00 +0000

The advancement of self-driving technology is driven by the need for robust perception and navigation systems. Simulators for autonomous driving facilitate the rapid development and testing of navigation algorithms; however, a key issue for most is their inaccurate modeling of the radar sensor. This is a significant drawback as radars offer robust sensing capabilities in adverse weather conditions and occlusions. CARLA, a widely adopted open-source simulator, provides a simplistic radar model that fails to capture the complex physical and material-dependent behavior of real-world radar. To address these limitations, we present CShenron, a radar simulation framework integrated into CARLA, which generates realistic radar measurements by fusing LiDAR and camera data. C-Shenron also supports configurable radar parameters, multiple sensor placements, and scalable dataset generation. Our evaluations demonstrate that radar-camera fusion models, trained with C-Shenron’s generated data,...

Demo Abstract: C-Shenron: A Realistic Radar Simulation Framework for CARLA

Thu, 29 Jan 2026 00:00:00 +0000

The advancement of self-driving technology is driven by the need for robust and efficient perception systems along with frameworks for End-to-End testing, enabled by the CARLA simulator. We introduce C-Shenron, a novel integration of a realistic radar sensor model within CARLA, enabling researchers to develop and test navigation algorithms using radar data. It is the first realistic radar simulator which utilizes LiDAR and camera sensors to generate high-fidelity radar ADC measurements from physics based modeling of the environment. Utilizing this radar sensor and showcasing its capabilities in simulation, we demonstrate improved performance in end-to-end driving scenarios. Our setup aims to rekindle the interest in radar-based self-driving research and promote the development of algorithms that leverages its strengths.

Pixnapping: Bringing Pixel Stealing out of the Stone Age

Thu, 15 Jan 2026 00:00:00 +0000

Pixel stealing attacks enable malicious websites to leak sensitive content displayed in victim websites. The idea, introduced by Stone in 2013, is to embed victim websites in iframes and use SVG filters to compute on, and create side channels as a function of, those websites' pixels. Fortunately, despite the danger, pixel stealing attacks are all but mitigated today thanks to websites and web browsers heavily restricting iframes and cross-origin cookie sharing. This paper introduces a pixel stealing framework targeting Android devices that bypasses all browser mitigations and can even steal secrets from non-browser apps. Our key observation is that Android APIs enable an attacker to create an analog to Stone-style attacks outside of the browser. Specifically, a malicious app can force victim pixels into the rendering pipeline via Android intents and compute on those victim pixels using a stack of semi-transparent Android activities. Crucially, our framework enables stealing secrets...

Supporting autistic children and adolescents’ social communication skills through digital technologies: A systematic literature review

Fri, 5 Dec 2025 00:00:00 +0000

This literature review examines the role of digital technologies in supporting the development of social communication skills in autistic children and adolescents. Grounded in sociocultural and activity theory, this review analyzes thirty-one peer-reviewed articles to assess how technology supports autistic children and adolescents to develop these skills. The skills were categorized into five major groups based on the definitions provided in the reviewed articles. The findings suggest that various digital tools, such as virtual reality, have been used to deliver social interventions targeting skills like initiating a conversation. Some studies that combined two technologies, such as augmented reality and video modeling, reported positive results. While these technologies have shown promise in enhancing targeted skills, no single tool can address all social communication skills areas, indicating the need for tailored interventions that meet individual needs. This review informs...

Prerequisites and Performance in a Machine Learning Course: A Quantitative Analysis

Thu, 4 Dec 2025 00:00:00 +0000

Demand for Machine Learning (ML) courses remains high, and educators face open questions about which prerequisites are important for student success in upper-year ML courses. Prior work has shown that instructors and students in ML courses believe that the math prerequisites and their relative recency are barriers to success, but this relationship has not been demonstrated quantitatively. In this paper, we study the link between prerequisite grades and performance in an upper-year ML course at two sites. We use linear models to study the extent to which student grades in prerequisite courses in calculus, linear algebra, statistics, and software design are predictive of student performance in the ML course. We consider the effect of additional factors like gender, first-in-family status, prior experience, comfort with mathematics, and comfort with academic English. Like prior work in many domains, and consistent with ML instructor and student perspectives, we find that prerequisite...

Author Correction: Greengenes2 unifies microbial data in a single reference tree

Thu, 4 Dec 2025 00:00:00 +0000

Author Correction: Greengenes2 unifies microbial data in a single reference tree

Hilby - Hilbert Interactive Prefix Plots

Thu, 20 Nov 2025 00:00:00 +0000

Hilbert curves are a common method to visualize data related to IP address spaces. In this demo, we present Hilby, a React framework to create such visualizations both for IPv4 and IPv6. Hilby offers a new perspective on Hilbert curves by enabling interactive aggregation and deaggregation of prefixes of different lengths simultaneously. By combining this with other features, e.g., color, Hilby can display both fine details and coarse overviews of large amounts of multidimensional networking data in the same frame without sacrificing performance or user experience. We provide use cases where visualizations benefit from Hilbys capabilities in research and practice.

Evolution of Programmers' Trust in Generative AI Programming Assistants

Thu, 20 Nov 2025 00:00:00 +0000

Motivation. Trust in generative AI programming assistants is a vital attitude that impacts how programmers use those programming assistants. Programmers that are over-trusting may be too reliant on their tools, leading to incorrect or vulnerable code; programmers that are under-trusting may avoid using tools that can improve their productivity and well-being. Methods. Since trust is a dynamic attitude that may change over time, this study aims to understand programmers’ evolution of trust after immediate (one hour) and extended (10 days) use of GitHub Copilot. We collected survey data from 71 upper-division computer science students working on a legacy code base, representing a population that is about to enter the workforce. Leveraging existing survey instruments and open-ended free response questions, we quantitatively measure student trust levels and qualitatively uncover why student trust changes. Findings. Student trust, on average, increased throughout the study. After completing...

Unveiling IPv6 Scanning Dynamics: A Longitudinal Study Using Large Scale Proactive and Passive IPv6 Telescopes

Thu, 20 Nov 2025 00:00:00 +0000

We introduce new tools and vantage points to develop and integrate proactive techniques to attract IPv6 scan traffic, thus enabling its analysis. By deploying the largest-ever IPv6 proactive telescope in a production ISP network, we collected over 600M packets of unsolicited traffic from 1.9k Autonomous Systems in 10 months. We characterized the sources of unsolicited traffic, evaluated the effectiveness of five major features across the network stack, and inferred scanners' sources of target addresses and their strategies.

BASTION: A Framework for Secure Third-Party IP Integration in NoC-based SoC Platforms

Thu, 6 Nov 2025 00:00:00 +0000

Modern System-on-Chip (SoC) architectures are a complex mix of processors, accelerators, memories, and I/O controllers interconnected by on-chip communication networks. Given the complexity of the computation and the requirements mandated in modern applications, several of these IPs are often outsourced as third-party modules. The integration of third-party modules, however, has been demonstrated to raise severe system-level security concerns – undiscovered vulnerabilities, incorrect firmware configurations, malicious code, and hardware trojans undetected in such IPs can produce leaks of confidential information and compromise the integrity of critical components. These challenges are further intensified when the communication infrastructure lacks robust mechanisms to supervise and monitor the interactions of third-party IPs with the rest of the system. Thus, runtime monitoring and supervising of third-party IPs is a crucial aspect for the system-level security of the entire SoC...

Greater than the Sum of its LUTs: Scaling Up LUT-based Neural Networks with AmigoLUT

Thu, 6 Nov 2025 00:00:00 +0000

Applications like high-energy physics and cybersecurity require extremely high throughput and low latency neural network (NN) inference. Lookup-table-based NNs address these constraints by implementing NNs as lookup tables (LUTs), achieving inference latency on the order of nanoseconds. Since LUTs are a fundamental FPGA building block, LUT-based NNs efficiently map to FPGAs. LogicNets (and its successors) form one class of LUT-based NNs that target FPGAs, mapping neurons directly to LUTs to meet low latency constraints with minimal resources. However, it is difficult to build larger, more performant LUT-based NNs like LogicNets because LUT usage increases exponentially with respect to neuron fan-in (i.e., number of synapses X synapse bitwidth). A large LUT-based NN quickly runs out of LUTs on an FPGA. Our work AmigoLUT addresses this issue by creating ensembles of smaller LUT-based NNs that scale linearly with respect to the number of models. AmigoLUT improves the scalability...

Bilinear Classes: A Structural Framework for Provable Generalization in RL